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vbm: Variance-Based Sensitivity Analysis for Weighting Estimators

Provides methods for variance-based sensitivity analysis and weighting estimators in observational studies based on methodology by Huang & Pimentel (2025) <doi:10.1093/biomet/asae040>. Includes bootstrap inference, bias bounds estimation, and visualization tools for sensitivity parameters.

Version: 0.1.0
Imports: parallel, magrittr, dplyr, WeightIt, estimatr, ggplot2, scales
Suggests: jointVIP, knitr, rmarkdown, pkgdown, cobalt, osqp
Published: 2026-06-30
DOI: 10.32614/CRAN.package.vbm (may not be active yet)
Author: Jiayao Gan [aut, cre], Melody Huang [aut], Samuel D. Pimentel [aut], Andy A. Shen [aut], National Science Foundation [fnd] (Grant #2142146)
Maintainer: Jiayao Gan <u3612852 at connect.hku.hk>
BugReports: https://github.com/Staniks0/vbm/issues
License: MIT + file LICENSE
URL: https://github.com/Staniks0/vbm
NeedsCompilation: no
CRAN checks: vbm results

Documentation:

Reference manual: vbm.html , vbm.pdf
Vignettes: Variance-Based Sensitivity Analysis for Weighting Estimators (source, R code)

Downloads:

Package source: vbm_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries: r-release (arm64): vbm_0.1.0.tgz, r-oldrel (arm64): vbm_0.1.0.tgz, r-release (x86_64): not available, r-oldrel (x86_64): vbm_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=vbm to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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